OpenRaiser/Intern-Atlas huggingface dataset:
One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
Venue: arXiv cs.CL 2019
Year: 2019

We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type `location:armed-group' based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.
Machine Translation Evaluation with BERT Regressor
Venue: arXiv cs.CL 2019
Year: 2019

We introduce the metric using BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019) for automatic machine translation evaluation. The experimental results of the WMT-2017 Metrics Shared Task dataset show that our metric achieves state-of-the-art performance in segment-level metrics task for all to-English language pairs.
A Practical Guide for the Effective Evaluation of Twitter User Geolocation
Venue: arXiv cs.SI 2019
Year: 2019

Geolocating Twitter users---the task of identifying their home locations---serves a wide range of community and business applications such as managing natural crises, journalism, and public health. Many approaches have been proposed for automatically geolocating users based on their tweets; at the same time, various evaluation metrics have been proposed to measure the effectiveness of these approaches, making it challenging to understand which of these metrics is the most suitable for this task. In this paper, we propose a guide for a standardized evaluation of Twitter user geolocation by analyzing fifteen models and two baselines in a controlled experimental setting. Models are evaluated using ten metrics over four geographic granularities. We use rank correlations to assess the effectiveness of these metrics. Our results demonstrate that the choice of effectiveness metric can have a substantial impact on the conclusions drawn from a geolocation system experiment, potentially leading experimenters to contradictory results about relative effectiveness. We show that for general evaluations, a range of performance metrics should be reported, to ensure that a complete picture of system effectiveness is conveyed. Given the global geographic coverage of this task, we specifically recommend evaluation at micro versus macro levels to measure the impact of the bias in distribution over locations. Although a lot of complex geolocation algorithms have been applied in recent years, a majority class baseline is still competitive at coarse geographic granularity. We propose a suite of statistical analysis tests, based on the employed metric, to ensure that the results are not coincidental.
On the Nisan-Ronen conjecture for submodular valuations
Venue: arXiv cs.GT 2019
Year: 2019

We consider incentive compatible mechanisms for a domain that is very close to the domain of scheduling $n$ unrelated machines: the single exception is that the valuation of just one machine is submodular. For the scheduling problem with such cost functions, we give a lower bound of $Ω(\sqrt{n})$ on the approximation ratio of incentive compatible deterministic mechanisms. This is a strong information-theoretic impossibility result on the approximation ratio of mechanisms on relatively simple domains. The lower bound of the current work assumes no restriction on the mechanism side, but an expanded class of valuations, in contrast to previous general results on the Nisan-Ronen conjecture that hold for only special classes of mechanisms such as local, strongly monotone, and anonymous mechanisms. Our approach is based on a novel characterization of appropriately selected smaller instances that allows us to focus on particular type of algorithms (linear mechanisms), from which we extract a locality property that gives the lower bound.
Propose-and-Attend Single Shot Detector
Venue: arXiv cs.CV 2019
Year: 2019

We present a simple yet effective prediction module for a one-stage detector. The main process is conducted in a coarse-to-fine manner. First, the module roughly adjusts the default boxes to well capture the extent of target objects in an image. Second, given the adjusted boxes, the module aligns the receptive field of the convolution filters accordingly, not requiring any embedding layers. Both steps build a propose-and-attend mechanism, mimicking two-stage detectors in a highly efficient manner. To verify its effectiveness, we apply the proposed module to a basic one-stage detector SSD. Our final model achieves an accuracy comparable to that of state-of-the-art detectors while using a fraction of their model parameters and computational overheads. Moreover, we found that the proposed module has two strong applications. 1) The module can be successfully integrated into a lightweight backbone, further pushing the efficiency of the one-stage detector. 2) The module also allows train-from-scratch without relying on any sophisticated base networks as previous methods do.
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
Venue: arXiv cs.LG 2019
Year: 2019

Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point. During this process, the adversarial example can be further optimized, even when it has already been wrongly classified with 100% confidence, thus making the adversarial example even more difficult to detect. For this kind of adversarial examples, which we refer to as over-optimized adversarial examples, we discovered that the logits of the model provide solid clues on whether the data point at hand is adversarial or genuine. In this context, we first discuss the masking effect of the softmax function for the prediction made and explain why the logits of the model are more useful in detecting over-optimized adversarial examples. To identify this type of adversarial examples in practice, we propose a non-parametric and computationally efficient method which relies on interquartile range, with this method becoming more effective as the image resolution increases. We support our observations throughout the paper with detailed experiments for different datasets (MNIST, CIFAR-10, and ImageNet) and several architectures.
Packing Rotating Segments
Venue: arXiv cs.CG 2019
Year: 2019

We show that the following variant of labeling rotating maps is NP-hard, and present a polynomial approximation scheme for solving it. The input is a set of feature points on a map, to each of which a vertical bar of zero width is assigned. The goal is to choose the largest subsets of the bars such that when the map is rotated and the labels remain vertical, none of the bars intersect. We extend this algorithm to the general case where labels are arbitrary objects.
IPRE: a Dataset for Inter-Personal Relationship Extraction
Venue: arXiv cs.CL 2019
Year: 2019

Inter-personal relationship is the basis of human society. In order to automatically identify the relations between persons from texts, we need annotated data for training systems. However, there is a lack of a massive amount of such data so far. To address this situation, we introduce IPRE, a new dataset for inter-personal relationship extraction which aims to facilitate information extraction and knowledge graph construction research. In total, IPRE has over 41,000 labeled sentences for 34 types of relations, including about 9,000 sentences annotated by workers. Our data is the first dataset for inter-personal relationship extraction. Additionally, we define three evaluation tasks based on IPRE and provide the baseline systems for further comparison in future work.
Prudence When Assuming Normality: an advice for machine learning practitioners
Venue: arXiv cs.LG 2019
Year: 2019

In a binary classification problem the feature vector (predictor) is the input to a scoring function that produces a decision value (score), which is compared to a particular chosen threshold to provide a final class prediction (output). Although the normal assumption of the scoring function is important in many applications, sometimes it is severely violated even under the simple multinormal assumption of the feature vector. This article proves this result mathematically with a counter example to provide an advice for practitioners to avoid blind assumptions of normality. On the other hand, the article provides a set of experiments that illustrate some of the expected and well-behaved results of the Area Under the ROC curve (AUC) under the multinormal assumption of the feature vector. Therefore, the message of the article is not to avoid the normal assumption of either the input feature vector or the output scoring function; however, a prudence is needed when adopting either of both.
CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis
Venue: arXiv cs.CV 2019
Year: 2019

Computer vision based object tracking has been used to annotate and augment sports video. For sports learning and training, video replay is often used in post-match review and training review for tactical analysis and movement analysis. For automatically and systematically competition data collection and tactical analysis, a project called CoachAI has been supported by the Ministry of Science and Technology, Taiwan. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-based real-time microscopic competition data collection based on broadcast competition video. Machine learning techniques will be used to develop a tactical analysis. To reveal data in more understandable forms and to help in pre-match training, AR/VR techniques will be used to visualize data, tactics, and so on. In addition, training auxiliary devices including smart badminton rackets and connected serving machines will be developed based on the IoT technology to further utilize competition data and tactical data and boost training efficiency. Especially, the connected serving machines will be developed to perform specified tactics and to interact with players in their training.
Slot Based Image Augmentation System for Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Object Detection has been a significant topic in computer vision. As the continuous development of Deep Learning, many advanced academic and industrial outcomes are established on localising and classifying the target objects, such as instance segmentation, video tracking and robotic vision. As the core concept of Deep Learning, Deep Neural Networks (DNNs) and associated training are highly integrated with task-driven modelling, having great effects on accurate detection. The main focus of improving detection performance is proposing DNNs with extra layers and novel topological connections to extract the desired features from input data. However, training these models can be computationally expensive and laborious progress as the complicated model architecture and enormous parameters. Besides, the dataset is another reason causing this issue and low detection accuracy, because of insufficient data samples or difficult instances. To address these training difficulties, this thesis presents two different approaches to improve the detection performance in the relatively light-weight way. As the intrinsic feature of data-driven in deep learning, the first approach is "slot-based image augmentation" to enrich the dataset with extra foreground and background combinations. Instead of the commonly used image flipping method, the proposed system achieved similar mAP improvement with less extra images which decrease training time. This proposed augmentation system has extra flexibility adapting to various scenarios and the performance-driven analysis provides an alternative aspect of conducting image augmentation
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
Venue: arXiv cs.CV 2019
Year: 2019

Deep convolutional neural network based image super-resolution (SR) models have shown superior performance in recovering the underlying high resolution (HR) images from low resolution (LR) images obtained from the predefined downscaling methods. In this paper we propose a learned image downscaling method based on content adaptive resampler (CAR) with consideration on the upscaling process. The proposed resampler network generates content adaptive image resampling kernels that are applied to the original HR input to generate pixels on the downscaled image. Moreover, a differentiable upscaling (SR) module is employed to upscale the LR result into its underlying HR counterpart. By back-propagating the reconstruction error down to the original HR input across the entire framework to adjust model parameters, the proposed framework achieves a new state-of-the-art SR performance through upscaling guided image resamplers which adaptively preserve detailed information that is essential to the upscaling. Experimental results indicate that the quality of the generated LR image is comparable to that of the traditional interpolation based method, but the significant SR performance gain is achieved by deep SR models trained jointly with the CAR model. The code is publicly available on: URL https://github.com/sunwj/CAR.
Unsupervised Separation of Dynamics from Pixels
Venue: arXiv cs.CV 2019
Year: 2019

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way. We introduce a probabilistic model that first generate noisy positions for each object through a separate linear state-space model, and then renders the positions of all objects in the same image through a highly non-linear process. Such a linear representation of the dynamics enables us to propose an inference method that uses exact and efficient inference tools and that can be deployed to query the model in different ways without retraining.
Object as Distribution
Venue: arXiv cs.CV 2019
Year: 2019

Object detection is a critical part of visual scene understanding. The representation of the object in the detection task has important implications on the efficiency and feasibility of annotation, robustness to occlusion, pose, lighting, and other visual sources of semantic uncertainty, and effectiveness in real-world applications (e.g., autonomous driving). Popular object representations include 2D and 3D bounding boxes, polygons, splines, pixels, and voxels. Each have their strengths and weakness. In this work, we propose a new representation of objects based on the bivariate normal distribution. This distribution-based representation has the benefit of robust detection of highly-overlapping objects and the potential for improved downstream tracking and instance segmentation tasks due to the statistical representation of object edges. We provide qualitative evaluation of this representation for the object detection task and quantitative evaluation of its use in a baseline algorithm for the instance segmentation task.
Capivara: A decentralized package version control using Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

Distributed consensus and Blockchains are popular among the cryptocurrencies where no one except the coins users, owns the data and transactions. No different to open source repositories, where the data belongs to the users. In this work it is presented a manner of having a repository for software packages in a Blockchain with distributed consensus, supported by the idea of the also demonstrated proof-of-download.
Bilateral Operators for Functional Maps
Venue: arXiv cs.CV 2019
Year: 2019

A majority of shape correspondence frameworks are based on devising pointwise and pairwise constraints on the correspondence map. The functional maps framework allows for formulating these constraints in the spectral domain. In this paper, we develop a functional map framework for the shape correspondence problem by constructing pairwise constraints using point-wise descriptors. Our core observation is that, every point-wise descriptor allows for the construction a pairwise kernel operator whose low frequency eigenfunctions depict regions of similar descriptor values at various scales of frequency. By aggregating the pairwise information from the descriptor and the intrinsic geometry of the surface encoded in the heat kernel, we construct a hybrid kernel and call it the bilateral operator. Analogous to the edge preserving bilateral filter in image processing, the action of the bilateral operator on a function defined over the manifold yields a descriptor dependent local smoothing of that function. By forcing the correspondence map to commute with the Bilateral operator, we show that we can maximally exploit the information from a given set of pointwise descriptors in a functional map framework.
A performance comparison of Dask and Apache Spark for data-intensive neuroimaging pipelines
Venue: arXiv cs.DC 2019
Year: 2019

In the past few years, neuroimaging has entered the Big Data era due to the joint increase in image resolution, data sharing, and study sizes. However, no particular Big Data engines have emerged in this field, and several alternatives remain available. We compare two popular Big Data engines with Python APIs, Apache Spark and Dask, for their runtime performance in processing neuroimaging pipelines. Our evaluation uses two synthetic pipelines processing the 81GB BigBrain image, and a real pipeline processing anatomical data from more than 1,000 subjects. We benchmark these pipelines using various combinations of task durations, data sizes, and numbers of workers, deployed on an 8-node (8 cores ea.) compute cluster in Compute Canada's Arbutus cloud. We evaluate PySpark's RDD API against Dask's Bag, Delayed and Futures. Results show that despite slight differences between Spark and Dask, both engines perform comparably. However, Dask pipelines risk being limited by Python's GIL depending on task type and cluster configuration. In all cases, the major limiting factor was data transfer. While either engine is suitable for neuroimaging pipelines, more effort needs to be placed in reducing data transfer time.
Efficient Method for Categorize Animals in the Wild
Venue: arXiv cs.CV 2019
Year: 2019

Automatic species classification in camera traps would greatly help the biodiversity monitoring and species analysis in the earth. In order to accelerate the development of automatic species classification task, "Microsoft AI for Earth" have prepared a challenge in FGVC6 workshop at CVPR 2019, which called "iWildCam 2019 competition". In this work, we propose an efficient method for categorizing animals in the wild. We transfer the state-of-the-art ImagaNet pretrained models to the problem. To improve the generalization and robustness of the model, we utilize efficient image augmentation and regularization strategies, like cutout, mixup and label-smoothing. Finally, we use ensemble learning to increase the performance of the model. Thanks to advanced regularization strategies and ensemble learning, we got top 7/336 places in the final leaderboard. Source code of this work is available at https://github.com/Walleclipse/iWildCam_2019_FGVC6
Deformable Filter Convolution for Point Cloud Reasoning
Venue: arXiv cs.CV 2019
Year: 2019

Point clouds are the native output of many real-world 3D sensors. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxelize the points, which can result in a loss of local geometric details that cannot be recovered. In this paper, we propose a novel learnable convolution layer for processing 3D point cloud data directly. Instead of discretizing points into fixed voxels, we deform our learnable 3D filters to match with the point cloud shape. We propose to combine voxelized backbone networks with our deformable filter layer at 1) the network input stream and 2) the output prediction layers to enhance point level reasoning. We obtain state-of-the-art results on LiDAR semantic segmentation and producing a significant gain in performance on LiDAR object detection.
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Venue: arXiv cs.RO 2019
Year: 2019

Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines these modalities which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. In this work, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot.
Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations
Venue: arXiv cs.HC 2019
Year: 2019

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.